Network Security Management Research Articles

Bibliometrics Analysis of Event Management in the Digital Era: Challenges and Opportunities for Event Planners

To remain competitive, event planners must stay current with the latest trends and technologies, such as virtual and augmented reality, artificial intelligence and the Internet. This paper presents a comprehensive bibliometric analysis using VOSviewer software to provide insight into the intellectual structure of knowledge using citation and co-citation networks, as well as key concepts using a keyword co-occurrence network, on the topic of opportunities and challenges faced by event planners when managing events in the digital era. The investigation begins by examining the Scopus database for articles published with keywords relevant to the topic. VOSviewer software was used for science mapping and network analysis of extracted data. This systematic field mapping helps graphically illustrate the publication evolution between 2015 and 2024 and identify the current research interests and potential directions for future research. A total of 125 articles were included comprising 44 conference papers, 39 original articles, 9 book chapters, 5 books, and 2 reviews. The United States, United Kingdom and Germany were the principal countries of origin of publications. The most frequently appearing keywords were “information management” with 116 occurrences and “network security” with 68 occurrences. The top five keywords with the greatest total link strength were information management, network security, security information and event management, cybersecurity, and intrusion detection. It is imperative that stakeholders, including funding agencies, event management organizations, researchers, and event managers, collaborate to ensure that the volume of quality research in this area keeps pace with the rapid advancements transforming the industry.

A novel approach for application classification with encrypted traffic using BERT and packet headers

Recent years have seen substantial advancements in Internet technology along with environmental changes, which have led to the emergence of various security issues. There is also a trend of explosive growth in applications that encrypt network traffic for various types of services. Therefore, the classification of applications within encrypted traffic represents an important research issue for both secure network management and efficient bandwidth management. In such encrypted traffic, the payload itself is encrypted, and it is no longer viable to classify applications based on signatures extracted from plaintext. Most applications in public datasets for encrypted traffic classification are collected with the same IP address and port number, which makes the 5-tuple information a strong identifier. However, this 5-tuple contains many characteristics related to both the traffic collection environment and user-specific traits, rather than intrinsic features of the applications themselves. Therefore, when addressing the problem of encrypted traffic application classification, it is advisable to utilize header information excluding the 5-tuple and payload. Therefore, this paper proposes a novel service type and application classification system based on the Bidirectional Encoding Representation Transformer (BERT), which utilizes packet header information from encrypted traffic. The proposed system ensures the accuracy and generalization performance of the classification model by using only the header information from traffic packets, excluding the 5-tuple and payload. Further, to preserve the characteristics and semantic meaning of an encrypted traffic packet, sentences embedded with 2-byte tokens were used as input for BERT. The proposed system was designed to exclude labeling information from all sentences during the pre-training phase before proceeding with training. Fine-tuning was then conducted to align the system with the objectives of the service type and application classification. This experiment utilized the publicly available ISCX VPN-nonVPN dataset, and the proposed model achieved remarkable accuracy in the key performance measure, i.e., F1-scores, with values of 99.24 % in service type classification and 98.74 % in application classification. This capability can be used in maintaining the confidentiality of encrypted traffic, network security monitoring, Quality of Service (QoS), and traffic management in complex IT environments.

Network Functions in Cloud: Kubernetes Deployment Challenges

The rapid evolution of cloud computing has paved the way for deploying network functions (NFs) in cloud environments, significantly enhancing the flexibility, scalability, and efficiency of modern network infrastructures. Kubernetes, an open-source container orchestration platform, has emerged as a leading tool for deploying and managing these cloud-based network functions. However, despite its widespread adoption, Kubernetes presents several deployment challenges specific to network functions, stemming from its design, scalability, and operational intricacies. This paper delves into the core challenges faced during the deployment of network functions on Kubernetes, focusing on issues related to network performance, security, service orchestration, and resource management. The abstract aims to provide an overview of the technical hurdles and propose potential strategies to overcome them, thus contributing to the optimization of Kubernetes-based NF deployments in cloud environments. By analyzing existing literature and case studies, the paper identifies key areas where improvements are needed and discusses the implications of these challenges for the future of cloud-based network functions. Ultimately, the paper seeks to guide network architects and cloud engineers in better understanding the complexities of Kubernetes deployments for network functions and in developing more effective strategies for successful implementation.

A Two-Fold Traffic Flow Model for Network Security Management

A Two-Fold Traffic Flow Model for Network Security Management

Hybrid algorithm GA-PSO-BP in secure localization technology for wireless sensor networks

ABSTRACT Wireless sensors face problems such as poor positioning accuracy in their applications. A hybrid indoor space safety positioning technology is proposed for this purpose. This technology uses genetic models and particle swarm optimization models to optimize the parameters of neural networks. Considering the vulnerability of wireless sensors to Sybil attacks during the positioning process, communication encryption and location awareness are used for optimization. In the positioning experiment, the proposed method had maximum positioning errors of 0.23 m and 0.43 m on the first and second floors, respectively, which were superior to other methods. In the positioning security experiment, the positioning errors of the first and second floors after being attacked by Sybil were 4 m and 5 m respectively, while the proposed security positioning technology had an error range of 1 m. In terms of security comparison, when there are 31–35 attack samples, the proposed method has a maximum root mean square error and average absolute error of 0.72 m and 0.79 m, respectively, which is superior to other models. It can be seen that the proposed technology has excellent positioning effect and stability. The research content will provide important technical support for the practical deployment and security management of wireless sensor networks.

Analysis of problems and countermeasures in computer network engineering security

This paper analyzes the existing security issues in computer network engineering and proposes a series of countermeasures. To address the problems of hacker attacks and information leakage, it is recommended to strengthen network monitoring and the application of encryption technology. For the issues of malware and virus dissemination, it is advised to regularly update antivirus software and enhance employee security awareness training. Regarding network data leakage and privacy breaches, the use of secure transmission protocols and multi-factor authentication technology is advocated. For network security management and maintenance issues, establishing a sound security management system and emergency response plans is recommended to ensure the stable operation of network systems. Through the implementation of the above measures, the security level of computer network engineering can be effectively improved, ensuring the normal operation of network systems and the security of information.

Integrating optical security management with optical‐layer controller architecture for enhanced network security

AbstractTo guarantee the availability, confidentiality, and integrity of data transferred over optical channels—especially in the context of fifth‐generation (5G) communication infrastructure—optical network security management is essential. This paper provides an overview of security management for optical networks, emphasizing the importance of this practice in today's communication infrastructure and the difficulties presented by ever changing cyberthreats. The architecture of optical security management is shown, with special attention to how well it integrates with current optical‐layer controllers and how it facilitates the coordination of security operations among optical networks. The study also looks at use cases for optical network security management in 5G networks, such as safe data transfer, defense against cyberattacks, maintaining privacy in 5G apps, network slicing security, and resistance to physical assaults. Such instances highlight the adaptability and significance of optical network security management in bolstering 5G networks' security, privacy, and resilience across a range of businesses and applications.

Wireless sensor network security management based on virtual resource scheduling model

ABSTRACT For lofting the performance of wireless sensor networks, a lightweight gradient elevator algorithm model is improved using a virtual resource scheduling model and sparrow search algorithm particle swarm optimization algorithm. A new network intrusion detection method (SSAPSO-LightGBM) has been proposed to improve detection accuracy and efficiency. Then, the data security transmission level and user historical priority resource scheduling model (SDT-HRU-VRSM) are used to schedule resources to ensure network resource revenue. The results show that the SSAPSO LightGBM algorithm has a detection accuracy of 99.61% for Normal, 98.42% for R2L, 97.03% for U2R, 96.01% for Probe, and 98.41% for DoS. The SDT-HRU-VRSM model not only meets the needs of secure data transmission, but also takes into account the stickiness of increasing high-quality users. And according to security requirements, the number of virtual resource scheduling is increased, reducing the number of service network slicing instances and improving security. This model can effectively increase the attack cost of malicious users and increase network revenue, which is crucial for ensuring the security and efficient operation of wireless sensor networks.

Campus Network Security Intrusion Detection Based on Feature Segmentation and Deep Learning

At present, the secure campus network strategy adopts technical means such as distinguishing applications and limiting them separately, but they have triggered other new problems, greatly affecting the unity of network resources and data. The relatively dispersed network architecture will inevitably limit the further development and expansion of the campus network. Therefore, when universities plan their networks, they must consider whether the network is safe, complete, smooth, and sustainable for smooth upgrading and development. In order to improve the effect of campus network security intrusion detection, this paper combines feature segmentation and deep learning technology to construct a campus network security intrusion detection model. To reduce the transmission time of query requirements in the grid, this paper improves the replica management mechanism and requires the information server to cache the Bloom Filter structure of nodes in its successor node list. Moreover, this paper uses the Compressed Bloom Filter algorithm to compress the Bloom Filter structure that needs to be transmitted, therefore reducing the network traffic generated during the update process of the Bloom Filter structure copy and avoiding network congestion. It also constructs a campus network security intrusion detection model based on feature segmentation and deep learning. Through experimental verification, the effectiveness of the system in intrusion detection, user evaluation, information processing, and other aspects is verified, and it has certain advantages compared to traditional algorithms. Through experimental research, it can be seen that the campus network security intrusion detection model based on feature segmentation and deep learning proposed in the paper can effectively improve the effect of campus network security monitoring. The method proposed in this article can not only be applied to campus network security, but also to the network security management of enterprises and other units, with certain scalability

LSTM network optimization and task network construction based on heuristic algorithm

This work aims to advance the security management of complex networks to better align with evolving societal needs. The work employs the Ant Colony Optimization algorithm in conjunction with Long Short-Term Memory neural networks to reconstruct and optimize task networks derived from time series data. Additionally, a trend-based noise smoothing scheme is introduced to mitigate data noise effectively. The approach entails a thorough analysis of historical data, followed by applying trend-based noise smoothing, rendering the processed data more scientifically robust. Subsequently, the network reconstruction problem for time series data originating from one-dimensional dynamic equations is addressed using an algorithm based on the principles of Stochastic Gradient Descent (SGD). This algorithm decomposes time series data into smaller samples and yields optimal learning outcomes in conjunction with an adaptive learning rate SGD approach. Experimental results corroborate the remarkable fidelity of the weight matrix reconstructed by this algorithm to the true weight matrix. Moreover, the algorithm exhibits efficient convergence with increasing data volume, manifesting shorter time requirements per iteration while ensuring the attainment of optimal solutions. When the sample size remains constant, the algorithm’s execution time is directly proportional to the square of the number of nodes. Conversely, as the sample size scales, the SGD algorithm capitalizes on the availability of more information, resulting in improved learning outcomes. Notably, when the noise standard deviation is 0.01, models predicated on SGD and the Least-Squares Method (LSM) demonstrate reduced errors compared to instances with a noise standard deviation of 0.1, highlighting the sensitivity of LSM to noise. The proposed methodology offers valuable insights for advancing research in complex network studies.

CETP: A novel semi-supervised framework based on contrastive pre-training for imbalanced encrypted traffic classification

Encrypted traffic classification (ETC) requires differentiated and robust traffic representation captured from content-agnostic and imbalanced traffic data for accurate classification, which is challenging but indispensable for enabling network security and network management. Some existing deep-learning based ETC approaches have achieved promising results, but have limitations in real-world network environments: (1) label bias caused by traffic class imbalance and (2) traffic homogeneity due to component sharing. How to leverage open-domain unlabeled imbalanced traffic data to learn representation with strong generalization ability remains a key challenge. In this paper, we propose a novel imbalanced traffic representation model, called Contrastive Encrypted Traffic Pre-training (CETP), which pre-trains deep multi-granularity traffic representation from imbalanced data without directly using application labels. The pre-trained model can be further mitigated against label bias due to imbalance by semi-supervised continual fine-tuning via pseudo-label iterations and dynamic loss-weighting algorithms. CETP achieves state-of-the-art performance across four imbalanced encrypted traffic classification tasks, remarkably improving the F1 to 96.31% (2.74%↑) for CP-Android, 93.86% (3.58%↑) for CIC-2019, and 84.16% (10.19%↑) for ISCX-VPN. Meanwhile, we further validate the effectiveness of CETP in QUIC-based imbalanced encrypted traffic. Notably, we verify through analytical experiments that CETP not only effectively relieves label bias and homogeneous flow misclassification, but also extends to ETC methods with diverse feature extractors.

Critical Infrastructures at Risk Addressing Information Security in Power Systems

According to the power system network security risks and problems, this paper presents the work of information security and the general principles of information security from a technical and safety management put forward the idea of addressing security issues and methods of analysis of local area network security management related to issues, and according to their own experience of a relatively effective way of thinking.

DE-GNN: Dual embedding with graph neural network for fine-grained encrypted traffic classification

Nowadays, most network traffic is encrypted, which protects user privacy but complicates the task of analyzing and classifying encrypted traffic. Identifying the specific categories of encrypted traffic, such as application type or even the specific application, is of great significance for advanced network services and network security management. Many existing methods for encrypted traffic classification rely on machine learning and deep learning techniques, but they exhibit certain shortcomings. A considerable number of these methods rely on statistical features, which may lose their relevance as networks evolve and lead to the loss of important information. Additionally, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) face limitations in extracting features from encrypted traffic, specifically, their inability to learn traffic interaction information within a network flow.To address these challenges, we propose a model called Dual Embedding with Graph Neural Networks (DE-GNN) for fine-grained encrypted traffic classification. Based on the byte-packet-flow structure of network traffic, we present a dual embedding layer that encodes the packet header and payload separately using raw bytes, which allows subsequent processes to run separately and in parallel. Then, we develop the PacketCNN to extract packet-level features from both the header and payload. Afterwards, we construct a network flow as a Traffic Interaction Graphs (TIG) and utilize Graph Neural Networks (GNNs) to extract flow-level features. Finally, an adaptive deep feature fusion process is applied to combine flow-level features from the header and payload, creating a robust representation for classification. Extensive experiments are conducted on two well-known datasets, ISCX-VPN2016 and ISCX-Tor2016, to verify our approach. The experimental results demonstrate that DE-GNN effectively identifies the type of encrypted traffic, outperforming baselines significantly.

Computer Network Virus and Computer Network Security Prevention

With the rapid development of information technology, network security management is becoming increasingly complex, especially, from the point of view of data security, the protection of data should be strengthened, now, the popularity of computers and smart phones will bring a large amount of information, which is inextricably linked with our property and personal privacy, in the human society, with the continuous development of intelligent technology, people's personal information and property protection are also paying more and more attention. Therefore, in order to achieve the purpose of network security and preventing information leakage, it is necessary to analyze and categorize all kinds of data and build a unified management platform on this basis. In recent years, the development of China's computer technology has been more and more attention, especially with the rapid development of big data, data storage, processing capacity, security and other aspects of the rapid development of all walks of life has brought great economic benefits, and the development of social productivity has played a huge role in promoting, but also brought a huge security risks, but the combination of big data technology and computer network security is a good technology, and its advantages are fully reflected in daily life.

Design and implementation of marine information management network security system based on artificial intelligence embedded technology

The complexity of marine information types, data diversity, data collection difficulties and other aspects makes the network security of marine information management more and more prominent, and has become a major issue affecting the stability of the country and society, so it is urgent to establish a marine information management network security system. Traditional network security technology adopts a passive approach and cannot actively detect viruses, trojans, and other hidden objects in the network. Antivirus software would only be used when attacked. If the risk of network attack is too great, the consequences would be unimaginable. This paper designed a marine information management network security system based on artificial intelligence embedded technology, which improved the efficiency of marine information security management. This paper also applied the embedded technology of AI to the network security management, and proposed the k-means clustering algorithm (K-Means) of AI, which can greatly improve the network security. The experimental results in this paper showed that the intrusion detection rates of System 1 and System 2 were 56.3% and 78.3% respectively when the number of viruses was 50 at 30M, and 65.5% and 80.1% respectively when the number of viruses was 50 at 60M. It showed that the intrusion detection rate of System 2 was higher both at 30M and 60M.

Measuring network security in the cloud : A roadmap for proactive defense

An exhaustive examination and evaluation of hierarchy network security measuring and optimal active defense in the context of cloud computing is provided in this paper. After collecting all the cloud platform’s security-related data obtained via evaluation, the pertinent security data is summarized and analyzed to create the cloud platform’s safety record index. This index serves as a benchmark for the cloud platform administrators to evaluate the security vulnerabilities associated with their cloud platform. It offers cloud platform administrators a standard against which to assess the security hazards of cloud platforms. We primarily investigate the building of the cloud platform, the building process of the security situational awareness system, and the estimation of the safety circumstance value and use via the cloud platform’s security circumstance understanding system, thereby significantly enhancing the cloud platform’s stability, security, and dependability. By implementing the approach, the limitations of conventional network security management are circumvented, as the latter is predicated solely on historical data and is incapable of perceiving real-time alterations in the security condition of the system. Concurrently, the anticipated outcomes are incorporated into the imprecise decision-making system’s input, enhancing the evaluation’s precision. The approach improves the efficiency and efficacy of predicting the network safety situation in real time, augments the algorithm’s rate of convergence and prediction precision, and prevents overfitting from occurring. Simulation experiments conducted using the internet network safety posture dataset demonstrate that this research approach exhibits superior learning efficiency and lower prediction error compared to conventional machine learning and other deep learning techniques.

Exploring the Definition Mode and Problems of the Obligation in the Crime of Refusing to Fulfill the Obligation of Information Network Security Management—From the Perspective of Regulating Cyber Violence

Exploring the Definition Mode and Problems of the Obligation in the Crime of Refusing to Fulfill the Obligation of Information Network Security Management—From the Perspective of Regulating Cyber Violence

Research on the Crime of Refusing to Fulfill the Obligation of Information Network Security Management

Research on the Crime of Refusing to Fulfill the Obligation of Information Network Security Management

Исследование проблематики методик определения типа контента во входящем трафике

Content filtering in the context of cybersecurity and trusted environments is an important tactic used to ensure network security and functionality. It works by restricting access to certain websites, emails, files or other content that may contain harmful elements or pose a significant risk of infection. Content filtering ensures the security not only of an individual user's data, but also of an entire network of organizations and institutions, helping to minimize the risk of malicious security breaches. The study of methods for determining the type of content in incoming traffic is a relevant and important area in the field of information security and network analytics. In today's Internet space, a significant amount of data is transmitted through networks, and one of the key tasks is the classification of this traffic to ensure security and effective network management. Methods for determining the type of content in incoming traffic are a set of algorithms and approaches that allow you to automatically determine what type of data is transmitted over the network. In the course of studying the problems of methods for determining the type of content in incoming traffic, data on network traffic is collected, a data set is selected for training the model, we consider classifier algorithms and focus on metrics for assessing classification efficiency. The results of the study can be used to create effective systems for detecting malicious or unwanted content, filtering data, or optimizing the operation of network resources. The study of methods for determining the type of content in incoming traffic is of practical importance and can be applied in various fields, including information security, network analytics, monitoring of network resources and optimization of network processes.

Management mode and path of digital transformation of power grid enterprises based on artificial intelligence algorithm

Facing the arrival of the digital transformation (DT) era, power grid enterprises are currently grappling with pressures arising from economic cycles and structural factors, and for them, the integration and development of energy revolution and digital revolution are intertwined. At this stage, people have increasing requirements for power service quality. However, electric power enterprises (EPEs) are unable to meet such demands because of several limitations, such as the surge of power grid operation risk, insufficient systematic service level and unreasonable enterprise architecture. DT is an effective way for EPEs to improve their management and service level. Power grid enterprises should follow the trend of digital development and actively carry out DT. In simulating the operation of the power system, the first step is to establish a grid connection between the simulated generator and the simulated power grid. This involves adjusting the generator excitation to regulate the generator voltage and adjusting the output of the prime mover to regulate the generator frequency. This study explored the management mode and implementation path of DT of EPEs, proposed the DT management mode of EPEs based on artificial intelligence (AI) algorithms and conducted an experimental research. The research showed that the average of the four- month informatisation project management level index of the enterprise S simulation model was 3.02 % higher than that of the four-month informatisation project management level index of the enterprise T simulation model. The average value of the operation and maintenance (O&M) and service management index of the enterprise S simulation model was 3.14 % higher than that of the enterprise T simulation model. The improvement of the management level index and the average O&M of simulation models can enhance the operational efficiency of enterprises and provide them with clear development directions. The average value of production security management index, network security management index and data asset management index of the enterprise S simulation model was higher than those of the enterprise T simulation model. The DT management mode of power grid enterprises based on AI algorithms is an effective driving force for power supply enterprises to achieve modernisation and standardised development.